Can gamma be greater than 1 in a DQN?
$ \gamma $ goes up to 1, but cannot be greater than or equal to 1 (this would make the discounted reward infinite).

The discount factor $ \gamma $  determines the importance of future rewards. A factor of 0 will make the agent "myopic" (or short-sighted) by only considering current rewards, while a factor approaching 1 will make it strive for a long-term high reward. If the discount factor meets or exceeds 1, the action values may diverge. For $ \gamma =1$, without a terminal state, or if the agent never reaches one, all environment histories become infinitely long, and utilities with additive, undiscounted rewards generally become infinite.

Source:https://en.wikipedia.org/wiki/Q-learning
https://cs.stanford.edu/people/karpathy/reinforcejs/puckworld.html